Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369432
R. Subbu, Gregory Russo, K. Chalermkraivuth, J. Celaya
A visual interactive multi-criteria decision-making method for partitioning a portfolio of assets into mutually exclusive categories is presented. The two principal decision categories are hold and sell - portfolio assets in the sell category are considered as potential sale prospects, and the other assets in the portfolio are considered as potential retention prospects. The problem may be mathematically formulated as a multi-criteria 0/1 knapsack problem with multiple constraints. The decision-making method centers on the utilization of several coupled 2D projections of the portfolio in the multi-dimensional criterion space. The decision-maker interacts with these projections in a variety of ways to express and record multi-category (hold, hold-bias, sell-bias, and sell) set partitioning preferences. The decision-maker may also set an aggregated preference threshold that is utilized for partitioning the portfolio into the two principal hold and sell categories. The decision-maker may further fine-tune their preferences and threshold settings so as to achieve a multitude of financial targets.
{"title":"Multi-criteria Set Partitioning for Portfolio Management: A Visual Interactive Method","authors":"R. Subbu, Gregory Russo, K. Chalermkraivuth, J. Celaya","doi":"10.1109/MCDM.2007.369432","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369432","url":null,"abstract":"A visual interactive multi-criteria decision-making method for partitioning a portfolio of assets into mutually exclusive categories is presented. The two principal decision categories are hold and sell - portfolio assets in the sell category are considered as potential sale prospects, and the other assets in the portfolio are considered as potential retention prospects. The problem may be mathematically formulated as a multi-criteria 0/1 knapsack problem with multiple constraints. The decision-making method centers on the utilization of several coupled 2D projections of the portfolio in the multi-dimensional criterion space. The decision-maker interacts with these projections in a variety of ways to express and record multi-category (hold, hold-bias, sell-bias, and sell) set partitioning preferences. The decision-maker may also set an aggregated preference threshold that is utilized for partitioning the portfolio into the two principal hold and sell categories. The decision-maker may further fine-tune their preferences and threshold settings so as to achieve a multitude of financial targets.","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"22 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113981095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369420
Kosuke Kato, M. Sakawa
In this paper, we focus on two-level integer programming problems with random variable coefficients in objective functions and/or constraints. Using chance constrained programming approaches in stochastic programming, the stochastic two-level integer programming problems are transformed into deterministic two-level integer programming problems. After introducing fuzzy goals for objective functions, we consider the application of the interactive fuzzy programming technique to derive a satisfactory solution for decision makers. Since several integer programming problems have to be solved in the interactive fuzzy programming technique, we incorporate a genetic algorithm designed for integer programming problems into it. An illustrative numerical example is provided to demonstrate the feasibility of the proposed method.
{"title":"Interactive fuzzy programming based on a probability maximization model using genetic algorithms for two-level integer programming problems involving random variable coefficients","authors":"Kosuke Kato, M. Sakawa","doi":"10.1109/MCDM.2007.369420","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369420","url":null,"abstract":"In this paper, we focus on two-level integer programming problems with random variable coefficients in objective functions and/or constraints. Using chance constrained programming approaches in stochastic programming, the stochastic two-level integer programming problems are transformed into deterministic two-level integer programming problems. After introducing fuzzy goals for objective functions, we consider the application of the interactive fuzzy programming technique to derive a satisfactory solution for decision makers. Since several integer programming problems have to be solved in the interactive fuzzy programming technique, we incorporate a genetic algorithm designed for integer programming problems into it. An illustrative numerical example is provided to demonstrate the feasibility of the proposed method.","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131396149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369115
Takeshi Uno, Kosuke Kato, H. Katagiri
This paper extends optimal location problems for emergency facilities to multiobjective programming problems by considering the following two objectives: one is to minimize the maximal distance of paths from emergency facilities to hospitals via accidents, and the other is to maximize frequency of accidents that emergency facilities can respond quickly. In order to find a satisfying solution of the formulated problems, an interactive fuzzy satisfying method with particle swarm optimization is proposed. Computational results for applying the method to examples of multiobjective emergency facility location problems are shown
{"title":"An Application of Interactive Fuzzy Satisficing Approach with Particle Swarm Optimization for Multiobjective Emergency Facility Location Problem with A-distance","authors":"Takeshi Uno, Kosuke Kato, H. Katagiri","doi":"10.1109/MCDM.2007.369115","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369115","url":null,"abstract":"This paper extends optimal location problems for emergency facilities to multiobjective programming problems by considering the following two objectives: one is to minimize the maximal distance of paths from emergency facilities to hospitals via accidents, and the other is to maximize frequency of accidents that emergency facilities can respond quickly. In order to find a satisfying solution of the formulated problems, an interactive fuzzy satisfying method with particle swarm optimization is proposed. Computational results for applying the method to examples of multiobjective emergency facility location problems are shown","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130299348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369444
W. Habenicht
We present a conceptual framework of an interactive method for solving integer linear vector optimization problems. The method is based on an enumerative cut approach. It combines cutting planes with enumerative parts. In this method the user can perform a structured searching process in the non-dominated set
{"title":"An interactive approach to integer linear vector optimization problems using enumerative cuts","authors":"W. Habenicht","doi":"10.1109/MCDM.2007.369444","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369444","url":null,"abstract":"We present a conceptual framework of an interactive method for solving integer linear vector optimization problems. The method is based on an enumerative cut approach. It combines cutting planes with enumerative parts. In this method the user can perform a structured searching process in the non-dominated set","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116048121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369416
M. J. Jesús, P. González, F. Herrera
This paper presents a multiobjective genetic algorithm for obtaining fuzzy rules for subgroup discovery. This kind of fuzzy rules lets us represent knowledge about patterns of interest in an explanatory and understandable form which can be used by the expert. The multiobjective algorithm proposed in this paper defines three objectives. One of them is used as a restriction on the rules in order to obtain a Pareto front composed of a set of quite different rules with a high degree of coverage over the examples. The other two objectives take into account the support and the confidence of the rules. The use of the mentioned objective as restriction allows us the extraction of a set of rules which describe more complete information on most of the examples. Experimental evaluation of the algorithm, applying it to a market problem shows the validity of the proposal obtaining novel and valuable knowledge for the experts
{"title":"Multiobjective Genetic Algorithm for Extracting Subgroup Discovery Fuzzy Rules","authors":"M. J. Jesús, P. González, F. Herrera","doi":"10.1109/MCDM.2007.369416","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369416","url":null,"abstract":"This paper presents a multiobjective genetic algorithm for obtaining fuzzy rules for subgroup discovery. This kind of fuzzy rules lets us represent knowledge about patterns of interest in an explanatory and understandable form which can be used by the expert. The multiobjective algorithm proposed in this paper defines three objectives. One of them is used as a restriction on the rules in order to obtain a Pareto front composed of a set of quite different rules with a high degree of coverage over the examples. The other two objectives take into account the support and the confidence of the rules. The use of the mentioned objective as restriction allows us the extraction of a set of rules which describe more complete information on most of the examples. Experimental evaluation of the algorithm, applying it to a market problem shows the validity of the proposal obtaining novel and valuable knowledge for the experts","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122446486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369431
M. Carroll, J. Josephson, James L. Russell
A communications network is represented as a graph of flow capacities. We study the problem of finding good network disruption attacks or target sets, i.e., a subset of vertices or edges that, once removed, impede communication between particular nodes. Multiple costs are associated with removing vertices or edges. Success in disrupting communications is traded off against the costs of the attack plans: the efficient frontier of attacks is estimated, and the results are studied in cross-linked diagrams. A multicriterial genetic algorithm is used to discover good plans for disrupting the communications network, where the genes correspond to nodes or links to be attacked. The genetic algorithm is seeded with an initial population of single-target genomes, one for each potential target. Multi-target attacks may be generated by breeding. Being on the efficient frontier guarantees a genome's survival to the next generation, so the population size is allowed to vary. The results are studied in interactive diagrams and in an "aggregate view" of the resulting population. Good attacks were found relatively rapidly, and the aggregate view revealed significant targets
{"title":"Tradeoffs on the Efficient Frontier of Network Disruption Attacks","authors":"M. Carroll, J. Josephson, James L. Russell","doi":"10.1109/MCDM.2007.369431","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369431","url":null,"abstract":"A communications network is represented as a graph of flow capacities. We study the problem of finding good network disruption attacks or target sets, i.e., a subset of vertices or edges that, once removed, impede communication between particular nodes. Multiple costs are associated with removing vertices or edges. Success in disrupting communications is traded off against the costs of the attack plans: the efficient frontier of attacks is estimated, and the results are studied in cross-linked diagrams. A multicriterial genetic algorithm is used to discover good plans for disrupting the communications network, where the genes correspond to nodes or links to be attacked. The genetic algorithm is seeded with an initial population of single-target genomes, one for each potential target. Multi-target attacks may be generated by breeding. Being on the efficient frontier guarantees a genome's survival to the next generation, so the population size is allowed to vary. The results are studied in interactive diagrams and in an \"aggregate view\" of the resulting population. Good attacks were found relatively rapidly, and the aggregate view revealed significant targets","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127962909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369414
M. Ida
In this paper we deal with multiobjective linear and quadratic programming problem with uncertain information. So far in the field of statistical analysis and data mining, e.g., mean-variance portfolio problem, support vector machine and their varieties, we have encountered various kinds of quadratic and linear programming problems with multiple criteria. Moreover coefficients in such problems have uncertainty that is expressed by interval, probabilistic distribution or possibilistic (fuzzy) distribution. In this paper, we define a robust basis for all possible perturbation of coefficients within intervals in objective functions and constraints that is regarded as secure and conservative solution under uncertainty. According to the conventional multi-objective programming literature, it is required to solve test subproblem for each basis. Therefore, in case of our interval problem excessive computational demand is estimated. In this paper investigating the properties of robust basis by means of combination of interval extreme points we obtained the result that the robust basis can be examined by working with only a finite subset of possible perturbations of the coefficients
{"title":"Robust Basis of Interval Multiobjective Linear and Quadratic Programming","authors":"M. Ida","doi":"10.1109/MCDM.2007.369414","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369414","url":null,"abstract":"In this paper we deal with multiobjective linear and quadratic programming problem with uncertain information. So far in the field of statistical analysis and data mining, e.g., mean-variance portfolio problem, support vector machine and their varieties, we have encountered various kinds of quadratic and linear programming problems with multiple criteria. Moreover coefficients in such problems have uncertainty that is expressed by interval, probabilistic distribution or possibilistic (fuzzy) distribution. In this paper, we define a robust basis for all possible perturbation of coefficients within intervals in objective functions and constraints that is regarded as secure and conservative solution under uncertainty. According to the conventional multi-objective programming literature, it is required to solve test subproblem for each basis. Therefore, in case of our interval problem excessive computational demand is estimated. In this paper investigating the properties of robust basis by means of combination of interval extreme points we obtained the result that the robust basis can be examined by working with only a finite subset of possible perturbations of the coefficients","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128809118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369441
R. Felix
A model of interactions between goals based on fuzzy relations for multiple goal decision making (attribute decision making) is presented. In contrast to other approaches, the interactive structure of goals for each decision situation is calculated explicitly based on fuzzy types of interaction. No preference relation defined on the power set of the decision alternatives is required. This helps not only to work with less complex initial information about the decision situation but also provides for a more efficient representation of the decision knowledge and for more efficient decision making procedures. Several real world applications based on the model are used in industry and finance
{"title":"Efficient Decision Making with Interactions Between Goals","authors":"R. Felix","doi":"10.1109/MCDM.2007.369441","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369441","url":null,"abstract":"A model of interactions between goals based on fuzzy relations for multiple goal decision making (attribute decision making) is presented. In contrast to other approaches, the interactive structure of goals for each decision situation is calculated explicitly based on fuzzy types of interaction. No preference relation defined on the power set of the decision alternatives is required. This helps not only to work with less complex initial information about the decision situation but also provides for a more efficient representation of the decision knowledge and for more efficient decision making procedures. Several real world applications based on the model are used in industry and finance","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129172155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369448
M. Jha, A. Maji
We develop a multi-objective approach to optimize 3-dimensional (3D) highway alignments using a genetic algorithm. Multi-objective genetic algorithms have been very popular for handling trade-offs among various objectives. The concept of Pareto optimally has been introduced in works and multi-objective genetic algorithms have been developed for this purpose. What we have found is that every problem is unique and there is no black box approach to implement multi-objective genetic algorithms in all problems. We implement the Pareto-optimality concept to develop a multi-objective genetic algorithm for the 3D highway alignment optimization problem on which we have worked for the last 10 years. We apply the multi-objective optimization approach to an example problem on which we had previously worked. The results suggest that the multi-objective approach has great promise for obtaining the best trade-off among various objectives to reach an optimal solution
{"title":"A Multi-Objective Genetic Algorithm for Optimizing Highway Alignments","authors":"M. Jha, A. Maji","doi":"10.1109/MCDM.2007.369448","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369448","url":null,"abstract":"We develop a multi-objective approach to optimize 3-dimensional (3D) highway alignments using a genetic algorithm. Multi-objective genetic algorithms have been very popular for handling trade-offs among various objectives. The concept of Pareto optimally has been introduced in works and multi-objective genetic algorithms have been developed for this purpose. What we have found is that every problem is unique and there is no black box approach to implement multi-objective genetic algorithms in all problems. We implement the Pareto-optimality concept to develop a multi-objective genetic algorithm for the 3D highway alignment optimization problem on which we have worked for the last 10 years. We apply the multi-objective optimization approach to an example problem on which we had previously worked. The results suggest that the multi-objective approach has great promise for obtaining the best trade-off among various objectives to reach an optimal solution","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130505272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-04-01DOI: 10.1109/MCDM.2007.369418
E. Faulkner
We describe a mechanism for optimal strategy generation from a Bayesian belief network (BBN). This system takes a BBN model either created by the user or derived from data. The user then specifies a set of goals (consisting of both objectives and constraints) and the observed and actionable variables in the model. The system then applies an optimizer to develop strategies that optimally achieve the specified goals. The system can be used by either human decision makers or autonomous agents. A distinguishing feature of the system is the ability to return strategies in the form of deterministic actions that result in the highest probability of achieving the desired goals. This allows the user to execute the strategies without further reasoning. In this paper we describe the architecture of the system and show examples of developing strategies from models created either by domain experts or directly from data
{"title":"Strategy Generation Under Uncertainty Using Bayesian Networks and Black Box Optimization","authors":"E. Faulkner","doi":"10.1109/MCDM.2007.369418","DOIUrl":"https://doi.org/10.1109/MCDM.2007.369418","url":null,"abstract":"We describe a mechanism for optimal strategy generation from a Bayesian belief network (BBN). This system takes a BBN model either created by the user or derived from data. The user then specifies a set of goals (consisting of both objectives and constraints) and the observed and actionable variables in the model. The system then applies an optimizer to develop strategies that optimally achieve the specified goals. The system can be used by either human decision makers or autonomous agents. A distinguishing feature of the system is the ability to return strategies in the form of deterministic actions that result in the highest probability of achieving the desired goals. This allows the user to execute the strategies without further reasoning. In this paper we describe the architecture of the system and show examples of developing strategies from models created either by domain experts or directly from data","PeriodicalId":306422,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115990088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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